What are the major steps in data mining?
• Derived attributes. It is rare for a model to built using only the attributes present in the cleaned data; rather, additional attributes called derived attributes are usually defined. As a single example, a stock on the S&P 500 has a price and an earnings associated with it, but the ratio of the price divided by the earnings is more important for many applications than either single attribute considered by itself. The construction of the derived and data attributes from the raw data is sometimes called shaping the data. • Modeling. Once the data is prepared and data mart is created, one or more statistical or data mining models are built. • Post-processing. It is common to normalize the outputs of data mining models and to apply business rules to the inputs and the outputs of the models. This is to ensure that the scores and other outputs of the models are consistent with the over all business processes the models are supporting. • Deployment. Once a statistical or data mining model
